Joey Grasty
0bec3bb3f0
ep3 uploaded to the team Drive folder and linked as the current production checkpoint. ep2's link is kept in place per request, labeled as the previous production checkpoint rather than removed.
3.5 KiB
Voice Bench
A browser tool for exploring the Vox Day / Christopher Nuttall SFT packages and generating scenes from them against the joint CPT+SFT model, including a STYLE-swap control for testing voice conditioning across authors.
Full training report (CPT + SFT setup, results, and evaluation data): https://claude.ai/code/artifact/d3415f56-3f9b-40de-aa70-ac0992549ced
What's here
voice-bench.html— the tool itself. It's a single self-contained file with no install step: open it in a browser (double-click it, or File > Open). It needs no server of its own — it just needs a model server to talk to (see below).start-model.sh— starts the model server (llama-server, from llama.cpp) thatvoice-bench.htmlsends generation requests to.
The 208 training/holdout packages themselves are already embedded inside
voice-bench.html — nothing else needs downloading for browsing.
One-time setup (per machine)
1. Get the model file
Download Qwen3-30B-A3B-VoxDay-Nuttall-SFT-ep3-Q8_0.gguf (~31GB) from
Google Drive — this is the current production checkpoint, switched from
ep2 on 2026-08-30 based on separability and length-calibration evidence
(see JOINT_TRAINING_CLOSEOUT_REPORT.md):
https://drive.google.com/open?id=1QZT488Uu1b9Pxoie9Hs2lnxtOhMD4Pwr
The previous production checkpoint, Qwen3-30B-A3B-VoxDay-Nuttall-SFT-ep2-Q8_0.gguf,
remains available for comparison:
https://drive.google.com/open?id=1q1w_XMp6oRmfx8xqXDV-HvDx_krVxQtD
Memory needed: ~31GB for the weights plus ~3GB of KV cache at the full
32768-token context, so ~35GB total. Confirmed working on a DGX Spark;
should comfortably fit a 48GB L40 as well. If a machine has less than that,
lower CTX_SIZE (below) before anything else.
2. Build llama.cpp with CUDA support
Needs an NVIDIA GPU with the CUDA toolkit and cmake already installed.
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j
This produces llama.cpp/build/bin/llama-server, which is what
start-model.sh runs.
3. Start the model server
MODEL_PATH=/path/to/Qwen3-30B-A3B-VoxDay-Nuttall-SFT-ep3-Q8_0.gguf \
LLAMA_SERVER=/path/to/llama.cpp/build/bin/llama-server \
./start-model.sh
Leave this running in a terminal — it's the process voice-bench.html
talks to. If you'd rather not set environment variables every time, edit the
defaults at the top of start-model.sh instead.
4. Open voice-bench.html
Open the file in a browser. The "Server" field in the top-right corner
defaults to http://127.0.0.1:8200, which is correct if the model server is
running on the same machine as the browser. If it's running on a different
machine on your network, change it to that machine's address instead, e.g.
http://192.168.1.50:8200 — and make sure that machine's firewall allows
inbound connections on the port (sudo ufw allow from <your-subnet> to any port 8200 proto tcp on Ubuntu).
Troubleshooting
- "SERVER UNREACHABLE" in the page header — the model server isn't running yet, is still loading the model (can take a minute for a 31GB file), or the Server field points at the wrong address/port.
- Out of memory when starting the server — re-run with a smaller
context, e.g.
CTX_SIZE=8192 ./start-model.sh. - Opening the HTML file from a typed path does something odd in
Firefox — use File > Open (Ctrl+O) instead of typing the path into the
address bar, or paste the full
file:///home/you/path/voice-bench.htmlURL.